{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "magnetic-brazil",
   "metadata": {},
   "source": [
    "## 样本不平衡"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "violent-paper",
   "metadata": {},
   "source": [
    "### 通过weight解决"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aerial-habitat",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.datasets import make_blobs #聚类产生数据集的方法"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "talented-dispatch",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x17031fc4978>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "class_1 = 1000 #类别1 样本1000个\n",
    "class_2 = 100  #类别2 样本100个\n",
    "centers = [[0,0],[2.0,2.0]]  #两个类别的中心店\n",
    "clusters_std = [2.5,0.5]  #两个类别的方差\n",
    "\n",
    "X,y = make_blobs(n_samples=[class_1,class_2],\n",
    "          centers=centers,\n",
    "          cluster_std=clusters_std,\n",
    "          random_state=420,shuffle=False)\n",
    "\n",
    "plt.scatter(X[:,0],X[:,1],c = y,cmap='rainbow',s = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "infectious-master",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight=None, criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#划分数据集\n",
    "from sklearn.model_selection import train_test_split\n",
    "Xtrain,Xtest,Ytrain,Ytest = train_test_split(X,y,test_size=0.2,random_state=420)\n",
    "\n",
    "#不设定class_weight\n",
    "clf_01 = DecisionTreeClassifier()\n",
    "clf_01.fit(Xtrain,Ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "stuffed-uncle",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(ccp_alpha=0.0, class_weight='balanced', criterion='gini',\n",
       "                       max_depth=None, max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort='deprecated',\n",
       "                       random_state=None, splitter='best')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#设定class_weight\n",
    "clf_02 = DecisionTreeClassifier(class_weight='balanced')\n",
    "clf_02.fit(Xtrain,Ytrain)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "conditional-claim",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_01.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "welsh-madness",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9045454545454545"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf_02.score(Xtest,Ytest)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "falling-complex",
   "metadata": {},
   "source": [
    "### 通过混淆矩阵解决"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "deadly-secretariat",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "stylish-astrology",
   "metadata": {},
   "source": [
    "混淆矩阵"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "solid-louis",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[184,   7],\n",
       "       [ 15,  14]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#平衡前\n",
    "metrics.confusion_matrix(Ytest,clf_01.predict(Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "educated-seller",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[182,   9],\n",
       "       [ 12,  17]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#平衡后\n",
    "metrics.confusion_matrix(Ytest,clf_02.predict(Xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "baking-minnesota",
   "metadata": {},
   "source": [
    "精准率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "scheduled-purse",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6666666666666666"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(Ytest,clf_01.predict(Xtest))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "engaged-presentation",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6538461538461539"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "metrics.precision_score(Ytest,clf_02.predict(Xtest))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fewer-principle",
   "metadata": {},
   "source": [
    "召回率"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "mediterranean-forward",
   "metadata": {},
   "outputs": [
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